AIMS: The main purpose of this study is to investigate the correlation between the average daily temperature and the rate of coronavirus epidemic growth in the infected regions. BACKGROUND: The rapid outbreak of the new Coronavirus (COVID-19) pandemic and the spread of the virus worldwide, especially in the Northern Hemisphere, have prompted various investigations about the impact of environmental factors on the rate of development of this epidemic. Different studies have called attention to various parameters that may have influenced the spread of the virus, and in particular, the impact of climatic parameters has been emphasized. OBJECTIVE: The main hypothesis object of our research is that between regions exhibiting a significant difference in the mean daily temperature, a significant difference is also observed in the average cumulative daily rate of confirmed cases and that this does not happen if there is no significant difference in mean daily temperature. METHOD: The research hypothesis was investigated through statistical analysis. The F-test was used to test whether there is significant equality of variances for each pair of case studies, and then, by the T- Test, the existence of a significant difference was investigated. In all statistical tests, the confidence level of 95% is considered. In order to minimize the impact on the results of factors like the policy of the government or cultural differences among countries (food, exercise, weight, etc.), three case studies within five countries, namely Iran, Italy, Germany, Spain, and United States were compared separately. RESULT: This statistical analysis shows that there is a correlation between the average temperature and the epidemic rate, and this is especially evident when differences in average daily temperature are significantly larger, as it happens for Bandar Abbas in Iran, Milan in Italy, Santa Cruz in Spain, and Los Angeles in the US. Besides, the analysis of the average air temperatures shows that the epidemic rates of COVID-19 were higher in the case studies with a lower average temperature. Instead, when no significant differences exist in the average daily temperature of two cities in the same country, there is no significant difference in the average cumulative daily rate of confirmed cases. CONCLUSION: In all five selected countries, we found that when there is a significant difference in the daily mean temperature between two regions of a country, a significant difference also exists in the average cumulative daily rate of confirmed cases. Conversely, if there are no significant differences in the mean daily temperature of two regions in the same country, no significant difference is observed in the average cumulative daily rate of confirmed cases for these regions. In conclusion, the results of this study support the research hypothesis and confirm the effectiveness of the proposed method for analysis of the epidemic rates.

The outbreak of the new Coronavirus (COVID-19) pandemic has prompted investigations on various aspects. This research aims to study the possible correlation between the numbers of swab tests and the trend of confirmed cases of infection, while paying particular attention to the sickness level. The study is carried out in relation to the Italian case, but the result is of more general importance, particularly for countries with limited ICU (intensive care units) availability. The statistical analysis showed that, by increasing the number of tests, the trend of home isolation cases was positive. However, the trend of mild cases admitted to hospitals, intensive case cases, and daily deaths were all negative. The result of the statistical analysis provided the basis for an AI study by ANN. In addition, the results were validated using a multivariate linear regression (MLR) approach. Our main result was to identify a significant statistical effect of a reduction of pressure on the health care system due to an increase in tests. The relevance of this result is not confined to the COVID-19 outbreak, because the high demand of hospitalizations and ICU treatments due to this pandemic has an indirect effect on the possibility of guaranteeing an adequate treatment for other high-fatality diseases, such as, e.g., cardiological and oncological ones. Our results show that swab testing may play a significant role in decreasing stress on the health system. Therefore, this case study is relevant, in particular, for plans to control the pandemic in countries with a limited capacity for admissions to ICU units.

The outbreak of the new Coronavirus (COVID-19) pandemic has prompted investigations on various aspects. This research aims to study the possible correlation between the numbers of swab tests and confirmed cases of infection, with special attention to the sickness level. The study is carried out with reference to the Italian case, but the result is of more general importance, in particular for countries with limited availability of ICUs (intensive care units). The statistical analysis shows correlation between the number of swab tests and those of daily positive cases, mild cases admitted to hospital, intensive care cases, recovery, and death rate, and provides a basis to carry on an AI study. The results were validated using a multivariate linear regression (MLR) approach. Our main result is the identification of a significant statistical effect of reduction of the pressure on the Health system as result of the increase of the tests. The relevance of this result is not confined to the COVID-19 outbreak, because the high demand of hospitalizations and ICU treatments due to this pandemic has an indirect effect on the possibility of guaranteeing an adequate treatment for other high-fatality disease, such as e.g. cardiological, and oncological. Our results show that swab testing may play a major role to decrease the stress on the Health system of a country. Therefore, this case study is relevant in particular for the planning of the control of the pandemic in countries with a limited capacity of admission to ICU’s units.

La expansión de la IA está reconfigurando el mercado laboral y la estructura económica global con una profundidad comparable a la de las revoluciones industriales. Estudios recientes -incluidos los de Hosseini y Lichtinger en EE. UU.- muestran que la adopción intensiva de IA puede reducir la demanda de empleo en tareas rutinarias, especialmente en contextos con estructuras productivas poco diversificadas. Esto se combina con un fenómeno amplio: la emergencia de una geografía del trabajo tecnológico, marcada por la concentración del valor añadido en países que controlan plataformas, algoritmos y hardware avanzado, mientras que economías dependientes afrontan una creciente vulnerabilidad. España, Italia y buena parte de América Latina comparten debilidades estructurales que las sitúan en una posición ambigua en esta transición: disponen de una base científica respetable, pero carecen de un tejido industrial robusto, muestran baja inversión en I+D y sufren polarización laboral. En este contexto, la IA actúa como un acelerador de tendencias preexistentes: amplifica desigualdades, tensiona los mercados laborales y profundiza las asimetrías entre el centro tecnológico global y las periferias. La cuestión central no es tecnológica, sino política y estratégica: cómo convertir la IA en motor de desarrollo endógeno y no en un nuevo ciclo de dependencia.